An approach is provided for machine learning-based map generation. The approach involves, for example, receiving an encoder output for an image depicting a geographic area, wherein the encoder output comprises an encoding for each tile of a plurality of tiles of the image, and wherein the encoding represents data associated with a location of each tile. The approach also involves using a machine learning decoder to determine a window over the encoder output comprising a tile of the plurality of tiles and one or more neighboring tiles and to process the encoding associated with the tile and the one or more neighboring tiles in the window to generate a map representation for the location of the tile and providing the map representation as an output.
A vehicle mirror assembly is provided. The vehicle mirror assembly, for example, includes a housing attachable to a vehicle. The vehicle mirror assembly also includes a reflective surface coupled to the housing or covering an opening in the housing. The vehicle mirror assembly further includes a movable sensor coupled to the housing or contained in a space formed by the housing and reflective surface. The vehicle mirror assembly further includes an actuator assembly configured to modify an orientation, a position, or a combination thereof of the movable sensor in response to a control signal.
An approach for vehicle diagnostics is provided. The approach, for example, involves receiving an indication identifying a need for a diagnostic scan. The approach also involves executing the diagnostic scan by initiating a movement of a movable sensor of a vehicle mirror assembly attached to a vehicle and acquiring diagnostic data during the movement of the movable sensor. The approach further involves analyzing the diagnostic data to generate a diagnostic report and providing the diagnostic report as an output.
G07C 5/08 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time
A system, a method, and a computer program product may be provided for determining a rooftop type. The system may be configured to obtain location information associated with a UAV, and obtain a first set of images and building data associated with a building in proximity of the UAV based on the location information. The system is configured to extract a set of rooftop features for a rooftop of the building using the first set of images and the building data. The set of rooftop features comprises at least a rooftop type and a durability parameter for the rooftop. The system is configured to store the set of rooftop features in conjunction with the building data for the building in a map database.
G01C 21/00 - Navigation; Navigational instruments not provided for in groups
B64C 39/02 - Aircraft not otherwise provided for characterised by special use
G05D 1/10 - Simultaneous control of position or course in three dimensions
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/774 - Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
G06V 20/17 - Terrestrial scenes taken from planes or by drones
5.
METHOD AND A SYSTEM FOR PREDICTING AN ACCIDENT OF A VEHICLE
The disclosure provides a method, a system, and a computer program product for predicting an accident. The method comprises capturing one or more audio signals based on one or more sensors onboard a vehicle. The method may include extracting one or more features associated with each of the one or more audio signals. The method further includes, generating an output for predicting the accident, based on the extracted one or more features associated with each of the one or more audio signals. Further, the output comprises a predicted accident state and an associated confidence value. Also, the predicted accident state comprising at least one of: a no accident state, a pre-accident state, a light accident state, and an intense accident state.
B60W 30/095 - Predicting travel path or likelihood of collision
B60W 40/08 - Estimation or calculation of driving parameters for road vehicle drive control systems not related to the control of a particular sub-unit related to drivers or passengers
6.
METHOD AND APPARATUS FOR ENHANCED LANE LINE ATTRIBUTION
A method, apparatus and computer program product are provided for generating lane line attributions for point locations along a road boundary for use in localization, map building, and map healing. Methods may include: receiving a sequence of geospatial observations from a vehicle traveling along a road segment; identifying, within the sequence of geospatial observations, lane line marking observations; determining, from the lane line marking observations, discrete points along the lane line markings, where the discrete points along the lane line markings include end points of a respective lane line marking; establishing a location of the vehicle based, at least in part, on the discrete points along the lane line markings; and providing for at least one of navigational assistance or at least semi-autonomous vehicle control based on the location established.
An apparatus, method, and computer program product are provided for communication node localization. For example, a digital fingerprint associated with a communication node in a communication environment is received. The digital fingerprint includes at least location data associated with a location estimate of the communication node and timestamp data associated with one or more measurements with respect to the communication node. Based at least in part on the timestamp data associated with the digital fingerprint, a frequency distribution data structure for the communication node is configured. Additionally, the location data is refined to generate refined location data for the communication node based on a comparison of one or more characteristics between the frequency distribution data structure and respective timetable data structures for respective scheduled events associated with an event location deemed to be at least proximate to the location data.
G01S 5/02 - Position-fixing by co-ordinating two or more direction or position-line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
8.
METHOD AND APPARATUS FOR RELIANCE UPON CENTRIPETAL ACCELERATION TO MITIGATE ERROR IN AN INERTIAL NAVIGATION SYSTEM
A method, apparatus and computer program product provide for error mitigation in an inertial navigation system (INS) based upon centripetal acceleration experienced by a vehicle that carries the INS. In the context of a method, the centripetal acceleration experienced by a vehicle carrying the INS that is making a turn is determined. The method also includes determining a velocity of the vehicle in reliance upon the centripetal acceleration. Based on the velocity determined in reliance upon the centripetal acceleration, the method further includes updating one or more filters of the INS to correct a velocity estimate provided by the INS.
G01C 21/16 - Navigation; Navigational instruments not provided for in groups by using measurement of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
9.
Systems and Methods for Detecting and Mapping LIDAR Blind Spots in Parking Garages
Disclosed is an approach for detecting LIDAR blind spots in parking garages comprising receiving, by one or more processors, floor layout data for at least a first floor of a parking garage, based on the received floor layout data for at least a first floor of a parking garage, detecting, by the one or more processors, at least one additional floor layout data for a floor of the parking garage, wherein the at least one additional floor has a substantially similar layout to the first floor of the parking garage, in response to detecting the at least one additional floor layout data for a floor of the parking garage, group by the one or more processors, the floors with substantially similar floor layout data into at least one cluster, making a determination, by the one or more processors, that at least one of the floor layout data in the cluster contains at least one LIDAR blind spot, and based at least on the determination, generating, by the one or more processors, LIDAR data for the at least one blind spot.
A method is provided automatically creating map objects from data from various sources gathered within a geographical area using data aggregation and conflation. Methods may include: receiving observation data associated with a geographic area; identifying at least one observed object within the observation data, the at least one observed object including an observed location and an observed object type; determining at least one confidence score for the at least one observed object; estimating similarity between the at least one observed object and at least one other observed object; merging the at least one observed object and the at least one other observed object; conflating the at least one observed object and the at least one other observed object to obtain a conflated object; providing for storage of the conflated object; and providing for at least one of navigational guidance or at least semi-autonomous vehicle control using the stored, conflated object.
Systems and methods for optimizing the notification of a modification to a route are provided. For example, a method of optimizing the notification of a modification to a route includes analyzing mobility data of an individual at a first location. The method also includes receiving at least one modification to a level of autonomous driving corresponding to one or more road segments between the first location and a second location. The method also includes, based on the at least one modification to the level of autonomous driving corresponding to the one or more road segments, determining one or more changes to at least one route between the first location and the second location. The method also includes providing a notification to the individual that includes information about the one or more changes to the at least one route between the first location and the second location.
Systems and methods for selecting an autonomous vehicle software application are provided. For example, a method for selecting an autonomous vehicle software application includes receiving information corresponding to a vehicle trip in a given vehicle. The method also includes determining one or more elements corresponding to at least one of the vehicle trip and the given vehicle. The method also includes based on the determined one or more elements, selecting an autonomous vehicle software application for operation of the given vehicle.
Embodiments described herein relate to anonymizing sensor data through the use of map data. Methods include: receiving sensor data defining a trajectory; map-matching the sensor data using a map-matching algorithm to a plurality of road segments of a map database to generate a sequence of map-matched sensor data elements; determining a first value representing anonymization associated with a start of the trajectory; determining a second value representing anonymization associated with an end of the trajectory; determining first map-matched sensor data elements at the start of the trajectory to be redacted based on the first value; determining second map-matched sensor data elements at the end of the trajectory to be redacted based on the second value; and transmitting sensor data associated with elements in the sequence of map-matched sensor data elements between the first map-matched sensor data elements and the second map-matched sensor data elements.
An approach is provided for spatial aggregation for location based services. The approach involves, for example, determining a plurality of partitions for a geographic area. The approach also involves determining a set of destinations that is common to a first partition and a second partition of the plurality of partitions. The set of destinations are associated with a plurality of trips originating from first partition, the second partition, or a combination thereof. The approach further involves determining a statistical property of the plurality of trips between any of the set of destinations and the first partition, the second partition, or a combination thereof. The approach further involves merging the first partition with the second partition into the traffic analysis zone based on the statistical property.
A method, apparatus, user interface, and computer program product are provided to receive an aerial image of a geographic region, identify at least one potential map object within the aerial image, determine at least one shadow area associated with the at least one potential map object within the aerial image, identify at least one camera proximate to the location of the at least one potential map object, capture image data of the area proximate to the least one potential map object via the at least one camera, and generate location data for the at least one map object or a road attribute based at least in part on the aerial image and image data of the area proximate to the least one potential map object or road attribute.
Systems and methods for determining a public transportation score are provided. For example, a method of determining a public transportation score includes receiving a public transportation metric associated with a first location within a public transportation network. The method also includes determining one or more elements of the public transportation network capable of affecting the public transportation metric. The method also includes determining a status of the one or more elements of the public transportation network. The method also includes based on the status of the one or more elements, determining a score corresponding to the public transportation metric.
An approach is provided for machine learning-based prediction of an estimated time of arrival (ETA) or any other trip characteristic. The approach involves, for example, receiving a request for an ETA (or any other trip characteristic). The request specifies an origin, a destination, and a time of departure. The approach also involves discretizing the origin to an origin ETA homogenous zone and the destination to a destination ETA homogenous zone. The approach further involves determining one or more features of one or more pre-computed k-shortest paths for an origin-destination (O-D) zone pair comprising the origin ETA homogenous zone and the destination ETA homogenous zone. The approach further involves providing the one or more features as an input to a trained machine learning to predict the ETA of the trip (or any other trip characteristic).
An apparatus, method and computer program product are provided for providing a the map including a binary representation of a weather-based hazard. In one example, the apparatus receives data indicating a polygon defining a region being impacted by a weather condition and overlays the polygon on a tile map. The apparatus receives attribute data indicating one or more functional classes of one or more road portions within each tile of the tile map that is partially covered by the polygon and determines a subset of tiles within the tile map that defines the weather condition based on the attribute data.
An approach is provided for a runaway vehicle detection system. The approach, for example, involves processing sensor data to detect that a vehicle is involved in or predicted to be involved in a runaway vehicle event. The approach also involves processing map data, the sensor data, or a combination thereof to determine a runaway vehicle safety location based on at least one attribute of the vehicle. The approach further involves generating an alert message indicating the runaway vehicle safety location. The approach further involves providing the alert message for display on a device associated with the vehicle. In one embodiment, the approach further involves mapping the runaway vehicle safety location, the runaway vehicle event, or a combination thereof to digital map of a geographic database.
A computing device, method and computer program product are provided to update the model of a building. In the context of a method, an image of a building and information regarding a location from which the image was captured are obtained. The method also includes causing a model of the building and the image of the building to be presented. Based on feedback from presentation of the model of the building and the image of the building, the method includes updating the model of the building by adjusting one or more parameters, including one or more vertical parameters, defining the model of the building. The method further includes causing the updated model of the building and the image of the building to be presented.
G06F 30/13 - Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
21.
METHODS AND SYSTEMS FOR IMPROVED DATA COLLECTION IN A POSITIONING SOLUTION THAT LEVERAGES CLOSED-LOOP LEARNING (CLL)
Disclosed is an approach for improved radio and/or altitude data collection in the context of a positioning system. In particular, processor(s) could execute a first phase of the collection process, which may involve collection of data only in seed area(s) associated with generation of an initial map as well as setting forth guidance, via a user interface of a mobile device, to collect data only in the seed area(s). And when the processor(s) detect a trigger to end the first phase, the processor(s) could responsively execute a second phase of the process, which may involve collection of data in secondary area(s) associated with generation of an updated map as well as setting forth guidance, via the user interface, (i) to collect data in the secondary area(s) and (ii) to visit seed area(s) and/or other area(s) that are deemed to be ready.
H04W 4/029 - Location-based management or tracking services
H04W 4/33 - Services specially adapted for particular environments, situations or purposes for indoor environments, e.g. buildings
G01S 5/02 - Position-fixing by co-ordinating two or more direction or position-line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
G06F 9/451 - Execution arrangements for user interfaces
22.
ERROR CHARACTERIZATION FOR GNSS-BASED POSITION ESTIMATES ON CONSTRAINED ROUTES
One or more processors obtain GNSS receiver data captured during a time period by a GNSS receiver disposed onboard a constrained vehicle traversing a constrained route. The processor(s) determine a GNSS-based position estimate based on the GNSS receiver data. The position estimate indicates an estimated location of the GNSS receiver during at least a portion of the time period. The processor(s) determine an error characterization corresponding to an offset between the position estimate and an expected location of the GNSS receiver along the constrained route. The processor(s) cause a mobile device to perform a positioning or navigation-related function that accounts for the error characterization.
G01S 19/40 - Correcting position, velocity or attitude
G01S 19/47 - Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial
G01S 19/46 - Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being of a radio-wave signal type
Systems and methods for determining a route for mood improvement are provided. For example, a method of determining a route for mood improvement includes determining a mood of an individual. The method also includes receiving a destination as an input. The method also includes determining a route from a current location of the individual to the destination via a plurality of road segments for improving the mood of the individual.
A method, apparatus, and user interface for road debris detection comprising, in some embodiments, obtaining image data of at least one navigable link and/or road debris, determining a road debris indicator based on the obtained image data, wherein the road debris indicator includes road debris classification data, and associating the determined road debris indicator with one or more navigable links to update a map layer of a geographic database.
G01C 21/00 - Navigation; Navigational instruments not provided for in groups
G06V 20/58 - Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
25.
METHOD AND APPARATUS FOR DETERMINING APPOINTMENT ATTENDANCE PROBABILITY
A method, apparatus, and user interface for determining appointment attendance probability comprising obtaining an indication of least a first location and second location of an end user; wherein the first location and second location are obtained at a predefined time interval, obtaining end user driving profile data, obtaining end user appointment data, determining appointment attendance probability based, at least in part, on the obtained first location, second location, end user appointment data, and end user driving profile data, and updating at least one database with the determined appointment attendance probability.
An apparatus, method and computer program product are provided for predicting improper parking events within electric vehicle charging locations. In one example, the apparatus receives input data indicating whether a first charging station is being used and a first pattern of frequency in which the first charging station is used. The apparatus generates an output data indicating a likelihood in which a first vehicle is occupying a first parking space designated for the first charging station and is electrically disconnected from the first charging station. The apparatus generates the output data as a function of the input data by using historical data indicating events in which second vehicles occupied second parking spaces designated for second charging stations and were electrically disconnected from the second charging stations. The historical data indicate a second pattern of frequency in which each of the second charging stations is used.
An apparatus obtains instances of positioning quality information comprising respective position estimates, respective positioning approach indicators, and respective characteristics of the respective position estimates. The apparatus partitions the instances of positioning quality information into a plurality of groups, where each group corresponds to a geographic area, and partitions the instances of each group into clusters corresponding to positioning approaches. For each respective cluster of each respective group, the apparatus determines a respective positioning index based at least in part on respective characteristics of the respective instances of positioning quality information assigned to the respective cluster. The apparatus provides at least one instance of positioning index information, such that performance of a positioning and/or navigation-related function for a mobile apparatus may be controlled at least in part by the instance of positioning index information.
An approach is provided for machine learning-based registration of imagery with different perspectives. The approach, for example, involves retrieving a first training image and a second training image. The first training image depicts a geographic area from a first perspective and the second training image depicts the geographic area from a second perspective. The approach also involves initiating a labeling of one or more ground truth correspondence masks between the first training image and the second training image. The one or more ground truth correspondence masks denote an image region of the first training image that matches a corresponding image region of the second training image or vice versa. The approach further involves using the one or more ground truth correspondence masks to train a machine learning model to determine one or more predicted correspondence masks between a first input image and a second input image.
An apparatus, method and computer program product are provided for predicting events in which vehicles damage roadside objects. In one example, the apparatus receives input data indicating a first roadside object and contextual information associated with the first roadside object. The apparatus causes a machine learning model to generate output data as a function of the input data, wherein the output data indicate a likelihood in which one or more first vehicles will damage the first roadside object. The machine learning model is trained to generate the output data as a function of the input data by using historical data indicating events in which second vehicles damaged second roadside objects. The historical data indicate first attributes associated with surroundings of the second roadside objects, second attributes associated with the second vehicles, and route maneuver information associated with the second vehicles.
An approach is provided path-based map matching of a sequence of probe points. The approach involves, for instance, projecting a probe point of the sequence of probe points to a plurality of element projections, wherein each of the plurality of element projections is a candidate projection of the probe point to a respective candidate location on a map-matched road segment. The approach also involves determining an optimal spatial boundary containing the plurality of element projections associated to the probe point. The approach further involves specifying a distance to the optimal spatial boundary from a previous node as a heuristic of a probabilistic map matcher for all of the plurality of element projections. The approach further involves executing the probabilistic map matcher based on the heuristic to determine a map matched path and providing the map matched path as an output.
Embodiments described herein relate estimating a privacy level of an anonymized dataset based on an anonymization strategy selected using parameters of the dataset that form a dataset profile. Methods may include: receiving a dataset defining at least one trajectory; determining parameters of the dataset; generating a profile of the dataset based on the parameters of the dataset to establish a dataset profile; identifying a predetermined profile corresponding to the dataset profile; determining an anonymization strategy corresponding to the predetermined profile; anonymizing the dataset using the anonymization strategy to generate an anonymized dataset; and publishing the anonymized dataset for use with location-based services. According to some embodiments, identifying the predetermined profile corresponding to the dataset profile includes identifying a predetermined profile having parameters within a predefined degree of similarity of the parameters of the dataset.
An approach is provided for detecting road obstruction intensity for location-based applications and services. The approach involves, for instance, collecting probe data associated with a road segment. The approach also involves processing the probe data to generate a time space diagram (TSD). The TSD plots the probe data according to distance from an origin point on the road segment over time. The approach further involves determining an intensity of the road obstruction based on the TSD. The approach further involves determining a diversion confidence for diverting a route from the road segment based on the intensity and providing the diversion confidence as an output.
An apparatus, method and computer program product are provided for predicting events in which drivers render aggressive behaviors while maneuvering vehicles. In one example, the apparatus receives input data indicating a target location and including attribute data associated with the target location. The apparatus causes a machine learning model to generate output data as a function of the input data. The output data indicate a likelihood in which a target driver will render an aggressive behavior at the target location while maneuvering a target vehicle. The machine learning model is trained to generate the output data as a function of the input data by using historical data indicating events in which drivers rendered the aggressive behavior while maneuvering vehicles.
A system, method, apparatus, computer readable medium, and user interface for generating a navigation route based on weather manipulation data comprising: receiving weather manipulation data for a geographic or a three-dimensional region associated with at least one candidate route, wherein the weather manipulation data indicates whether there is at least one manmade environmental factor present; and generating the navigation route based on the weather manipulation data for at least one candidate route.
A network apparatus, for example, obtains probe data corresponding to a respective vehicle making a late lane change while traversing a TME of a traversable network. Location information indicating a location of the late lane change is extracted from the probe data. Map, weather, and/or traffic data for the location is obtained. A late lane change feature description is generated based on information extracted from the probe data and the map, weather, and/or traffic data. A model is trained using a machine learning technique and training data comprising the late lane change feature description. The model is executed to generate a late lane change prediction corresponding to a TME of a digital map. The network apparatus causes at least one of (a) the digital map to be updated, (b) traffic data corresponding to the TME to be updated, or (c) a navigation-related function to be performed based on the prediction.
G08G 1/01 - Detecting movement of traffic to be counted or controlled
G01C 21/00 - Navigation; Navigational instruments not provided for in groups
G08G 1/04 - Detecting movement of traffic to be counted or controlled using optical or ultrasonic detectors
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
36.
METHOD AND APPARATUS FOR APPROXIMATING BORDER(S) BETWEEN CLUSTERS OF GEOSPATIAL POINTS
An approach is provided for approximating border(s) between clusters of geospatial points based on triangulation. The approach involves receiving geospatial points respectively associated with at least one of a plurality of codes representing an identifiable characteristic. The approach also involves tessellating the points to generate triangles. The approach further involves processing the triangles to determine triangle edge(s) that connects a first point associated with a first code set and a second point associated with a second code set. The approach further involves, for each of the determined triangle edge(s), adding a new point along the determined triangle edge and generating a new edge from the new point to a centroid of a respective triangle. The approach further involves determining a polygon based on the new edge. The polygon represents a border between the points associated with the first code set and the points associated with the second code set.
A method, apparatus, and user interface for providing a carjacking detection and prediction system is disclosed. For example, image data of at least one vehicle may be obtained and a carjacking indicator for the vehicle based on the obtained image data is determined. One or more road segments may then be identified, and the determined indicator is associated with the one or more identified road segments to update a map layer of a geographic database and/or perform other actions.
An apparatus, method and computer program product are provided for predicting overtaking maneuver events. In one example, the apparatus receives input data including attribute data associated with a location and first travel data associated with a first occupant of a first vehicle. The apparatus causes a machine learning model to generate output data as a function of the input data. The output data indicate a likelihood in which the first vehicle will execute an overtaking maneuver at the location. The machine learning model is trained to generate the output data as a function of the input data by using historical data indicating events in which second vehicles executed the overtaking maneuver. The historical data include second travel data associated with second occupants of the second vehicles.
An approach is provided for machine learning of vehicular wait events. The approach, for instance, involves processing a sensor observation to determine a wait event. The wait event indicates that at least one person is in a wait state. The approach also involves processing the sensor observation to determine one or more contextual features associated with a location of the wait event, a time of the wait event, the at least one person, or a combination thereof. The approach further involves determining a ground truth of the wait state. The approach further involves vectorizing the one or more contextual features and the ground truth into a training vector. The approach further involves using the training vector to train a machine learning model to determine predicted waiting data based on one or more input vectors. The approach further involves providing the trained machine learning model as an output.
An apparatus, method and computer program product are provided for predicting slipping events for micromobility vehicles. In one example, the apparatus receives input data indicating a target location and including contextual data associated with the target location. The apparatus causes a machine learning model to generate output data as a function of the input data. The output data indicate a likelihood in which a target micromobility vehicle will slip at the target location. The machine learning model is trained to generate the output data as a function of the input data by using historical data indicating events in which micromobility vehicles have slipped. The historical data indicate slip-inducing objects within locations of the events, proximity of sources of the slip-inducing objects relative to the locations, and one or more factors that cause the slip-inducing objects to be disposed within the locations.
A method, apparatus, and user interface for a lingering communication detection system comprising obtaining data of at least one end user electronic communication and determining a lingering communication indicator based on the obtained data, identifying one or more road segments, and associating the determined indicator with one or more identified road segments to update a map layer of a geographic database.
A method, apparatus and computer program product facilitate corrections to be made to a location determined utilizing a satellite-based positioning technique to reduce the errors introduced by atmospheric delay and/or advance of navigation signals propagating through an atmospheric layer, such as the ionosphere. In the context of a method for enabling a customized request for corrections to be made, the method includes determining a perimeter of a grid within which the corrections are sought and transmitting, toward a service provider, a request that identifies one or more correction points within the perimeter of the grid. In response to the request, the method includes receiving information associated with the corrections. The method also includes determining a position based upon the satellite-based positioning technique in combination with the received information associated with the corrections.
G01S 19/07 - Cooperating elements; Interaction or communication between different cooperating elements or between cooperating elements and receivers providing data for correcting measured positioning data, e.g. DGPS [differential GPS] or ionosphere corrections
G01S 19/24 - Acquisition or tracking of signals transmitted by the system
G01S 19/40 - Correcting position, velocity or attitude
G01S 19/05 - Cooperating elements; Interaction or communication between different cooperating elements or between cooperating elements and receivers providing aiding data
43.
METHOD AND APPARATUS FOR PLACING A SHARED MICRO-MOBILITY VECHILE IN PUBLIC SPACES
An approach is provided for determining placement location(s) in a public space for a shared micro-mobility vehicle. The approach involves identifying a candidate placement location in the sidewalk area. The approach also involves determining factor(s) associated with the location and/or nearby location(s) in the sidewalk area. The approach further involves making a determination of whether the location is suitable for temporary placement of a shared micro-mobility vehicle based on whether the location enables: (i) avoiding interference between the vehicle and a current/predicted people flow, a bicycle lane, and/or one or more objects, (ii) preventing the vehicle from hindering usage of a point of interest, a vehicle parking area, and/or a transit station, or (iii) accommodating the vehicle to a sidewalk configuration. The approach further involves based on the determination, outputting an indication of whether the location is suitable for temporary placement of the vehicle.
G08G 1/14 - Traffic control systems for road vehicles indicating individual free spaces in parking areas
G01S 17/89 - Lidar systems, specially adapted for specific applications for mapping or imaging
G08G 1/065 - Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
A system, a method, and a computer program product may be provided for processing event data. For example, event data relating to an occurrence of an event corresponding to a first tile is received, and a first count value associated with the received event data is determined. A threshold count value associated with the event within the first tile is obtained, and dynamic switching between a link-based processing and a tile-based processing is performed, based on a comparison between the first count value and the threshold count value, wherein on determining the first count value to be greater than the threshold count value, the tile-based processing is performed, and on determining the first count value to be lesser than the threshold count value, the link-based processing is performed.
A method, apparatus, and user interface for a vehicle window damage detection system comprising obtaining image data of at least one vehicle window and determining a window damage indicator based on the obtained image data, identifying one or more road segments, and associating the determined window damage indicator with one or more identified road segments to update a map layer of a geographic database.
An approach is provided for providing a charging time window for an electric vehicle based on environmental conditions at a charge point. The approach involves receiving a request to charge a battery of an electric vehicle at a charge point. The approach also involves determining a battery temperature operating range for the battery. The approach further involves determining a battery temperature function that predicts a temperature of the battery based on an environmental condition at the charge point. The approach further involves determining a charging time window for charging the battery based on the battery operating range and the battery temperature function. The approach further involves outputting the charging time window. The approach can involve determining a temperature regulating time period for the battery and/or the charge point to cool down or heat up before charging based on the battery temperature and/or the environmental condition at the charge point.
B60L 53/62 - Monitoring or controlling charging stations in response to charging parameters, e.g. current, voltage or electrical charge
B60L 53/66 - Data transfer between charging stations and vehicles
B60L 58/24 - Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries for controlling the temperature of batteries
H02J 7/00 - Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
H01M 10/48 - Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
47.
METHOD, APPARATUS, AND SYSTEM FOR MAPPING AND LEVERAGING USAGE DATA OF LIDAR ON MOBILE DEVICES
An approach is provided for generating and leveraging map layer(s) representing information associated with Light Detection and Ranging (LiDAR) usage on mobile devices. The approach, for instance, involves determining LiDAR usage data generated by one or more mobile devices. The approach also involves generating a map layer of a geographic database based on the LiDAR usage data. The map layer indicates one or more locations where a LiDAR device was used by the one or more mobile devices based on the LiDAR usage data. The approach further involves providing the map layer as an output.
An approach is provided for determining a lane marking confusion index based on lane confusion event detections. The approach, for example, involves detecting, by at least one processor, a number of instances of a lane confusion event associated with a location in a road network. The lane confusion event indicates presence of temporary lane markings in addition to standard lane markings at the location. The approach also involves determining, by the at least on processor, a lane marking confusion index for the location based on the detected number of instances of the lane confusion event in association with the location. The approach further involves storing, by the at least one processor, the lane marking confusion index as a map attribute of the location in a geographic database.
An apparatus, method and computer program product are provided for predicting events in which drivers fail to see curbs while the drivers are maneuvering vehicles. In one example, the apparatus receives vehicle attribute data associated with a first vehicle, map data indicating one or more attributes of a road portion including a first curb, and sensor data indicating an orientation of a first driver within the first vehicle. The apparatus causes a machine learning model to render an output as a function of the vehicle attribute data, the map data, and the sensor data. The output indicates a likelihood of which the first driver will not be able to see the first curb at the road portion when the first driver is maneuvering the first vehicle. The machine learning model is trained to predict the output based on historical data indicating events in which second drivers maneuvered second vehicles to encounter the first curb or one or more second curbs.
An approach is provided for providing a navigation route for a requesting entity to meet other entities at flexible meeting locations. The approach, for example, involves receiving a request to generate a navigation route to visit a plurality of entities, wherein one or more mobile entities of the plurality of entities are capable of moving from a current location during the navigation route. The approach also involves computing respective isolines around each mobile entity based on a travel distance, a travel time, or a combination thereof that each mobile entity is ready to travel from the current location of each mobile entity during the navigation route. The approach further involves connecting the respective isolines around each mobile entity to determine the navigation route. The approach further involves providing the navigation route as an output.
A method is provided for automatically creating road and lane geometry from images representing probe data within a geographical area using data aggregation and conflation. Methods may include: receiving first observation data associated with a geographic area; processing the first observation data through a first encoder to produce first embeddings; receiving second observation data associated with the geographic area; processing the second observation data through a second encoder to produce second embeddings; establishing contrastive loss between the first embeddings and the second embeddings; producing consistent embeddings from the first embeddings and the second embeddings using the contrastive loss; generating, from the consistent embeddings, map data reflecting the first observation data and the second observation data; and updating a map database with the map data.
A method is provided automatically creating map geometry from data from various sources gathered within a geographical area using data aggregation and conflation with statistical analysis. Methods may include: receiving observation data associated with a geographic area; rasterizing objects within the observation data onto corresponding channels in a raster image corresponding to the geographic area having a given resolution; determining a distribution of locations of the objects parameterized by values in different channels in the raster image from the observation data; extracting analytic geometries from the raster image; generating map geometry based on the extracted analytic geometries; and updating a map database with the generated map geometry.
A method is provided for generation of map geometry through rasterization of probe data over time, where the generated map geometry can be used for map element generation. Methods may include: receiving probe data from a plurality of probe apparatuses within a geographic area where the probe data includes location information and time information; rasterizing the received probe data to generate an image corresponding to the probe data associated with the geographic area, where each pixel of the image includes a property representing at least one component of the probe data; processing the image to obtain one or more map elements; and updating a map database with the one or more map elements.
An approach is provided for automatically detecting suspicious device/vehicle presence on non-navigable roads. The approach involves, for example, receiving positioning data associated with a vehicle or a device. The approach also involves processing the positioning data to determine that a location of the vehicle or the device is on a non-navigable road. The approach further involves designating the location on the non-navigable road as a suspicious location. The approach further involves providing an alert message based on the suspicious location as an output.
A method is provided to automatically create road and lane geometry from images representing map geometry within a geographical area using object detection. Methods may include: receiving a rasterized image representative of map geometry within a geographic area, where each pixel of the rasterized image includes a property representing at least one component of the map geometry; applying an object detection model to the rasterized image; generating a list of the bounding boxes together with classes of objects within the bounding boxes based on the object detection model; generating map data from the list of bounding boxes and the classes of objects within the bounding boxes; and updating a map in a map database with the map data.
G01C 21/00 - Navigation; Navigational instruments not provided for in groups
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
A method is provided for automatically creating road and lane geometry from images representing probe data within a geographical area using graph estimation. Methods may include: receiving a rasterized image representative of map geometry within a geographic area, where each pixel of the rasterized image includes a property representing at least one feature of the map geometry; identifying pixels within the rasterized image corresponding to nodes of one or more map elements as node pixels; determining, for a respective node pixel, presence of a next node based on the at least one property of the respective node pixel; determining for the respective node pixel, a location corresponding to the next node based on the at least one property of the respective node pixel; generating, from a sequence of node pixels, at least one map element; and updating a map in a map database with the at least one map element.
A method is provided to using a generative adversarial network to generate map geometry from images representing probe data. Methods may include: receiving a rasterized image representative of probe data within a geographic area, where each pixel of the rasterized image includes a property representing at least one component of the probe data; generating a prediction image of road features within the geographic area using a generative adversarial network based on trained model parameters and the rasterized image; determining one or more map elements based, at least in part, on the prediction image and georeferenced locations of the road features within the prediction image; and updating a map of the geographic area with one or more map elements.
G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 20/58 - Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
58.
METHOD AND APPARATUS FOR GENERATING PERSONALIZED SPLINES
A method, apparatus, and user interface are provided for generating personalized splines. For example, one or more geometries that represent a portion of a road network are identified and a first spline curve is calculated from the one or more geometries. One or more additional geometries that represent a different portion of the road network are then interpolated, by a processor, and the processor then calculates at least a second spline curve from the one or more additional geometries, wherein the second spline curve is derived, at least in part, from personalized driving preference data.
A method, apparatus, and user interface for providing a roadworks location detection system is disclosed. For example, image data of at least one roadworks sign may be obtained and a location for the roadworks project may be determined based on the obtained image data. One or more road segments may then be identified, and the determined roadworks location associated with the one or more identified road segments to update a map layer of a geographic database and/or perform other actions.
G01C 21/36 - Input/output arrangements for on-board computers
G01C 21/00 - Navigation; Navigational instruments not provided for in groups
G06V 20/58 - Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
60.
REAL-TIME LANE-LEVEL TRAFFIC PROCESSING SYSTEM AND METHOD
System and methods for real-time lane level traffic processing. A lane level map matcher is used to map match probe data from probes to the lane level. A lane level travel time aggregator determines an optimal method to allocate travel-time per-link for a probe trajectory that traverses at least one lane. A lane aggregator aggregates the probe trajectories and probe-path speeds per-lane and obtain the representative speed for the lane.
An approach is provided for extracting point-of-interest features based on depth sensor data captured by mobile devices. The approach involves, for instance, receiving a Light Detection and Ranging (LiDAR) scan of a location captured using a LiDAR sensor of a portable device. The approach also involves determining a context of the location, a point of interest (POI) associated with the location, or a combination thereof. The approach further involves selecting a feature recognition parameter based on the context. The feature recognition parameter performs a feature detection analysis of a scenery depicted in the scan. The approach further involves initiating the feature detection analysis of the scan based on the feature recognition parameter to identify a feature, an attribute of the feature, or a combination thereof in the scenery.
A method, apparatus and computer program product are configured to re-estimate misalignment between the body frame of an inertial measurement unit (IMU) and the vehicle frame following movement of a mobile device embodying the sensor(s) of the IMU relative to the vehicle. In a method, movement of the mobile device relative to the vehicle is detected. In response to detecting movement of the mobile device relative to the vehicle, the method identifies (i) a point in time prior to or corresponding to commencement of movement of the mobile device relative to the vehicle and (ii) a subsequent point in time at which the mobile device is stable relative to the vehicle. The method then determines the frame orientation between body and vehicle frames. Based upon the determined frame orientation between body and vehicle frames, the method also includes determining a navigation solution for the vehicle in reliance upon non-holonomic constraints.
G01C 21/16 - Navigation; Navigational instruments not provided for in groups by using measurement of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
63.
METHOD AND APPARATUS FOR ERROR MITIGATION IN AN INERTIAL NAVIGATION SYSTEM
A method, apparatus and computer program product are configured to mitigate error in an inertial navigation system (INS) that relies upon a reference system to determine or update filter parameters. In a method, a determination is made of (i) deviation or divergence of a filter of the INS and/or (ii) invalidity of observation(s) by the reference system. The method also identifies the filter parameters from a history buffer associated with a prior point in time at which performance of the filter is stable. The filter parameters were updated at least once based upon observation(s) by the reference system after the prior point in time. The method forward processes sensor samples of the INS obtained subsequent to the prior point in time using the filter parameters from the history buffer at which performance of the filter was stable to mitigate the error.
G01C 21/16 - Navigation; Navigational instruments not provided for in groups by using measurement of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
64.
APPARATUS AND METHODS FOR DETERMINING CHARGING EFFICIENCY RATES FOR SOLAR-POWERED VEHICLES
An apparatus, method and computer program product are provided for predicting charging efficiency rates for solar-powered vehicles. In one example, the apparatus receives historical data of events in which solar-powered vehicles equipped with solar panels were electrically charged by receiving solar beams at the solar panels. The historical data indicate factors of the events that induced objects forming on the solar panels and obstructing, at least in part, the solar beams. The apparatus uses the historical data to train a machine learning model to output a charging efficiency rate of a target solar-powered vehicle as a function of at least one attribute associated with a location.
G07C 5/08 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time
B60L 8/00 - Electric propulsion with power supply from forces of nature, e.g. sun or wind
G01C 21/00 - Navigation; Navigational instruments not provided for in groups
65.
METHOD, APPARATUS, AND SYSTEM FOR MULTIMODAL POSITION SELECTION FOR A TRACKING DEVICE
An approach is provided for multimodal position selection based on an aggregated previous position of a tracking device. The approach, for example, involves receiving a telemetry item from the tracking device. The tracking device is configured with a plurality of positioning technologies to generate position signals respectively. A plurality of positions of the tracking device are respectively determined using the position signals. The approach also involves based on determining that the tracking device is stationary by determining that at least one of the plurality of positions is less than a threshold distance from a previous position or an aggregated previous position of the tracking device, selecting a position from the plurality of positions that is closest to the aggregated previous position of the tracking device. The approach further involves assigning the selected position to the telemetry item. The approach further involves providing the telemetry item with the selected position as an output.
G06Q 10/08 - Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
G01S 5/00 - Position-fixing by co-ordinating two or more direction or position-line determinations; Position-fixing by co-ordinating two or more distance determinations
G01S 5/02 - Position-fixing by co-ordinating two or more direction or position-line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
H04W 4/029 - Location-based management or tracking services
66.
METHOD, APPARATUS, AND SYSTEM FOR ESTIMATING A LANE WIDTH
An approach is provided for lane width estimation from incomplete lane marking detections of a road lane. The approach, for example, involves generating one or more perpendicular lines respectively from location centers of one or more first lane marking detections. A respective lane marking detection represents at least a portion of a boundary of the road lane as a line delimited by two location data points in accordance with detections by at least one sensor device onboard at least one vehicle. The approach also involves identifying second lane marking detections that each respectively intersect one of the one or more perpendicular lines. The approach further involves selecting one or more candidate lane widths based on one or more respective distances from the location centers to the second lane marking detections. The approach further involves determining an estimated lane width of the road lane based on the one or more candidate lane widths.
A system for estimating a lane with of a local road is provided. The system, for example, obtains lane marking data using one or more sensors. Then, the system identifies at least one map link or topology associated with the obtained lane marking data based on map data representative of one or more map links or topologies and determines that the obtained lane marking data is associated with a particular road type based on the identified map link or topology. The system then determines (i) a first lane width dataset including one or more widths of a first category, and (ii) a second lane width dataset based including one or more widths of a second category. A probable lane width then estimated by the system as a function of the first lane width dataset and the second lane width dataset.
A system is disclosed for generating a storage lane marking for a map database. The system, for example, generates a candidate storage lane marking for a first topology based on: the candidate storage lane marking being (i) to the left of a leftmost through-traffic lane marking of the first topology or to the right of a rightmost through-traffic lane marking of the first topology, and (ii) on either start or end of the first topology. The system further makes a determination of whether the candidate storage lane marking is valid based on one or more of following: (i) whether one or more vehicle path locations are located within a polygon defined by (a) the candidate storage lane marking and (b) a corresponding edge of the first topology, or (ii) whether the candidate storage lane marking is located within another topology different from the first topology.
Provided herein are systems and methods for updating a map. Map data associated with a topology and sensor data corresponding to linear features on the topology are obtained and a key linear feature is identified in the linear features. The method further comprises generating an extension of the key linear feature and identifying a next link segment corresponding to a current link segment of the key linear feature. At least one intersection location between the extended key linear feature and a perpendicular from at least one of (i.) the current link segment or (ii.) the next link segment is determined. A new linear feature location is generated based on the current link segment, the at least one intersection location, and the map data. The map is updated based on the new linear feature location.
A method, apparatus and computer program product are provided to dynamically determine and convey contextual relevance measures for shared vehicles. In one embodiment, a method is provided. The method includes identifying a user seeking transportation to a destination and identifying shared vehicles configured for transporting the user. The method further includes obtaining environmental context data based at least in part on a geographic area within which the user and the destination are located. The method further includes generating a contextual relevance measure for each shared vehicle. The contextual relevance measure is generated according to at least one of the environmental context data, configuration data for the one or more shared vehicles, or profile data associated with the user. The method further includes, responsive to determining that the contextual relevance measure for a particular shared vehicle satisfies a configurable threshold, causing a physical configuration change for the particular shared vehicle
A method, apparatus, and user interface for providing a connection detection system is disclosed. For example, image data of at least one animate object is obtained, and an indicator of relationship for the animate object is determined based on the obtained image data. One or more road segments is then associated with the determined indicator to update a map layer of a geographic database.
A method, apparatus and computer program product are provided for learning to generate maps from raw geospatial observations from sensors traveling within an environment. Methods may include: processing geospatial observations from discrete trajectories through an iterative attention model incorporating a Gated Recurrent Unit gating pattern to obtain a feature summary; determining a drive offset for each of the discrete trajectories based on the feature summary; aligning the discrete trajectories to generate aligned geospatial observations based, at least in part, on the drive offset for a respective discrete trajectory; concatenating the aligned geospatial observations; processing the concatenated, aligned geospatial observations using at least one Set Transformer; generating, from the at least one Set Transformer, map geometries including objects from the geospatial observations; and providing for at least one of navigational assistance or at least semi-autonomous vehicle control based on the map geometries.
A method, apparatus and computer program product are provided for learning to generate maps from raw geospatial observations from sensors traveling within an environment. Methods may include: receiving a plurality of sequences of geospatial observations from discrete trajectories; aligning and concatenating the trajectories to generate concatenated, aligned geospatial observations; performing attentional clustering on the concatenated, aligned geospatial observations to obtain a set of entities; processing the set of entities using one or more Set Transformers; generating a preliminary feature set of map object geometries; determining a number of output entities from each of the one or more output heads based on statistics from the plurality of sequences of geospatial observations from the discrete trajectories and a count of the discrete trajectories; and outputting a feature set, the feature set including a number of map object geometries corresponding to a respective number of output entities for the respective output head.
A method, apparatus and computer program product are provided for learning to generate maps from raw geospatial observations from sensors traveling within an environment. Methods may include: receiving a plurality of sequences of geospatial observations from discrete trajectories; refining a summary entity set representation through iteration over discrete trajectories; determining a drive offset for each of the discrete trajectories based on a comparison of the summary entity set representation to a drive entity set for each of the discrete trajectories; aligning the discrete trajectories to generate aligned geospatial observations based, at least in part, on the drive offset for a respective discrete trajectory; concatenating the aligned geospatial observations; processing the concatenated, aligned geospatial observations using at least one Set Transformer; and generating, from the at least one Set Transformer, map geometries including objects from the geospatial observations.
A method, apparatus and computer program product are provided for learning to generate maps from raw geospatial observations from sensors traveling within an environment. Methods may include: receiving a plurality of sequences of geospatial observations from discrete trajectories; aligning the discrete trajectories generating aligned geospatial observations; concatenating the aligned geospatial observations; performing attentional clustering on the concatenated, aligned geospatial observations to obtain a set of entities with feature dimensionality; processing the set of entities through an iterative attention model incorporating a Gated Recurrent Unit gating pattern to obtain attentional layer outputs; generating, from one or more Set Transformers, a feature set of map object geometries based, at least in part, on the attentional layer outputs; updating a map geometry based on the feature set from the Set Transformers generating an updated map geometry; and provide for navigational assistance or at least semi-autonomous vehicle control based on the updated map geometry.
An apparatus, method and computer program product are provided for providing adaptive vehicle parking assistance. In one example, the apparatus receives sensor data indicating a user's interaction associated with a vehicle parking assistance feature. The vehicle parking assistance feature is a feature in which a vehicle causes a notification on a user interface in response to an object abutting or overlapping a virtual zone surrounding the vehicle. The apparatus determines a level of the user's familiarity on using the vehicle parking assistance feature based on the sensor data, and the apparatus adjusts a size of the virtual zone based on the level.
An approach is provided for reconstructing a road linear feature. The approach, for example, involves receiving two or more linear feature detections that respectively represent a linear feature of a road as a line segment delimited by two feature points. The approach also involves map matching the two or more linear feature detections to a road link segment of a geographic database. The approach further involves determining an orientation difference for each of the two or more linear feature detections based on an angle difference between each linear feature detection and a link orientation of the map matched road link segment. The approach further involves determining a feature orientation of the linear feature based on an aggregation of the orientation difference for each linear feature detection. The approach further involves constructing a representation of the linear feature based at least in part on the feature orientation.
A method, system, and computer readable storage medium are provided for predicting behavior of a vehicle. More specifically, image data of the vehicle is obtained by generating class prediction data for the vehicle and then identifying one or more related points of interest. The method, system, and computer readable storage medium then predict the behavior of the vehicle based upon the one or more points of interest and the class prediction data.
B60W 50/00 - CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit
G01C 21/00 - Navigation; Navigational instruments not provided for in groups
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
B60W 50/14 - Means for informing the driver, warning the driver or prompting a driver intervention
An apparatus, method and computer program product are provided for providing trailer sway detection. In one example, the apparatus receives sensor data and determines a pitch angle of a trailer based on the sensor data. The apparatus further determines a confidence of trailer sway occurring based on the pitch angle and in response to the confidence exceeding a threshold, the apparatus generates a warning signal indicating a potential trailer sway.
G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06T 7/70 - Determining position or orientation of objects or cameras
G01S 17/88 - Lidar systems, specially adapted for specific applications
80.
METHOD AND APPARATUS FOR ROAD SEGMENT TRAFFIC TENDENCY DETERMINATIONS
A method, apparatus and computer program product are provided to estimate a road segment traffic tendency determination value. A current traffic speed pattern data object may be generated for an initial location of a vehicle and a future traffic speed pattern data object may be generated for an estimated downstream location of the vehicle. A road segment traffic tendency determination value may then be estimated based at least in part on the current traffic speed pattern data object and the future traffic speed pattern data object. A road segment traffic tendency notification may be provided to the vehicle.
A method, apparatus, and computer program product are provided for using aerial imagery to identify and distinguish fluid leaks from objects, structures, and shadows in aerial imagery. A method may include: receiving an aerial image of a geographic region; identifying objects within the aerial image; determining shadow areas associated with the objects within the aerial image; determining whether areas exist contiguous with the shadow areas and extending beyond the shadow areas; identifying one or more fluid leaks in response to areas existing contiguous with the shadow areas and extending beyond the shadow areas; and generating an indication of the one or more fluid leaks including one or more locations of the one or more fluid leaks.
An approach is provided for generating a transaction proof of location. The approach, for example, involves collecting sensor data from one or more sensors of a device at a time, a location, or a combination thereof associated with a transaction. The sensor data represents one or more environmental observations of the location. The approach also involves generating a capsule of the one or more environmental observations and tagging the capsule with the time of the transaction. The approach further involves cryptographically signing the capsule and providing the cryptographically signed capsule as a proof of location.
H04L 9/32 - Arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system
83.
METHOD, APPARATUS, AND SYSTEM FOR POLE EXTRACTION FROM OPTICAL IMAGERY
An approach is provided for pole extraction from optical imagery. The approach involves, for instance, processing a plurality of images using a machine learning model to generate a plurality of redundant observations of a pole-like object and/or their semantic keypoints respectively depicted in the plurality of images. The approach also involves performing a photogrammetric triangulation of the plurality of redundant observations to determine three-dimensional coordinate data of the pole-like object and/or their semantic keypoints. The approach further involves providing the three-dimensional coordinate data of the pole-like object as an output.
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G01C 11/08 - Interpretation of pictures by comparison of two or more pictures of the same area the pictures not being supported in the same relative position as when they were taken
84.
DETECTING AND MONITORING DANGEROUS DRIVING CONDITIONS
System and methods for using probes to detect and elicit dangerous driving conditions. Historical probe data is aggregated and analyzed to identify reoccurring dangerous driving conditions down to the lane level. The lane level locations with high recurring dangerous driving condition metrics are identified and analyzed by a mapping system. The mapping system uses machine learning and other techniques to investigate, validate, and verify the dangerous driving conditions to ascertain the cause and then categorize the dangerous driving condition.
An approach is provided for traffic data blending based on road segment travel time reliability during traffic prediction. The approach involves, for instance, retrieving real-time traffic information and/or historical traffic information for each road segment within a geographic area. The approach also involves aggregating traffic flow speed data in the real-time traffic information to compute traffic pattern data for each road segment. The traffic pattern data includes static speed data of each said road segment. The approach further involves aggregating traffic flow speed data in the historical traffic information to compute travel time reliability index metric(s) for each said road segment. The approach further involves determining, based on the travel time reliability index metric(s), to use the traffic pattern data, a mean, or a percentile of a road segment travel time distribution in the historical traffic information for traffic prediction associated with each said road segment.
An approach is provided for detecting an on-boarding or off-boarding event based on mobile device sensor data. The approach, for example, involves retrieving sensor data collected from at least one sensor of a mobile device that is fixed in a stationary position relative to a vehicle. The approach also involves processing the sensor data to determine roll angle data for the vehicle over a time window. The approach further involves processing the roll angle data to determine one or more transitions of a roll angle value of the vehicle between one or more value levels. The approach further involves determining an on-boarding event, an off-boarding event, or a combination thereof based on the one or more transitions. The approach further involves providing the on-boarding event, the off-boarding event, or a combination thereof as an output.
B60W 40/08 - Estimation or calculation of driving parameters for road vehicle drive control systems not related to the control of a particular sub-unit related to drivers or passengers
G01C 9/06 - Electric or photoelectric indication or reading means
87.
METHOD, APPARATUS, AND SYSTEM FOR DETERMINING A BICYCLE LANE DEVIATION FOR AUTONOMOUS VEHICLE OPERATION
An approach is provided for determining bicycle lane deviations for autonomous vehicle warning or operation. The approach, for example, involves retrieving probe data associated with a bicycle transportation mode. The approach also involves determining a plurality of probe points of the probe data that are map-matched outside of a bicycle lane. The approach further involves clustering the plurality of probe points into at least one location cluster. The approach further involves storing the one or more location clusters in a geographic database as respective one or more hazard areas where a plurality of bicycles deviates outside of the bicycle lane. By way of example, the approach can further involve using the at least one location cluster to perform at least one of providing a warning message or determining a driving parameter for an autonomous vehicle.
System and methods for detecting and obtaining lane level insight in unplanned incidents. Probe-based vehicles and lane-level insight using a lane-level map-matcher are used to acquire information. This information is aggregated and used to differentiate lane activity in terms of traffic and safe navigation. With the identification of probes per-lane and probe speeds per-lane, sudden reductions in probe speeds may be obtained at a lane-based level. This is used to verify or detect lane-level incident or hazard warnings and consequently alert a driver to safer navigation paths ahead of time, like maneuver to a different lane or to take an alternative route.
A method, apparatus, and computer program product are provided for real-time map-matching of probe data. Methods may include: receiving a plurality of probe data points; receiving an indication of changes to the road network, where the indication of changes includes an addition of new road segments and a removal of closed road segments; map-matching the plurality of probe data points to one or more road segments of the road network based on closest candidate road segments to form map-matched probe data; in response to a closest candidate road segment for one or more probe data points of the plurality of probe data points corresponding to a closed road segment, map-matching the one or more probe data points to a next closest candidate road segment in the map-matched probe data; and updating dynamic map data with the map-matched probe data.
Systems and methods for determining an electric vehicle score are provided. For example, a method for determining an electric vehicle score includes receiving mobility pattern data corresponding to an individual. The method also includes analyzing one or more characteristics associated with an area based on the mobility pattern data. The method also includes determining an electric vehicle score corresponding to the individual based on the analysis of the one or more characteristics associated with the area.
A method, apparatus and computer program product are provided to determine one or more probe point data objects and/or validate one or more probe point data objects for use with determination of traffic conditions. A determined probe point data object may be determined and/or updated with one or more probe point data objects which exclude any probe point data objects determined to correspond to an off-road location. A validated probe point data object may additionally or alternatively be determined using one or more first validation sequence check operations and using one or more second validation sequence check operations. The determined probe point data object and/or validated probe point data object may be used to determine one or more traffic conditions, and as a result, traffic condition determinations may be determined more accurately and efficiently.
A method, apparatus and computer program product are provided to determine one or more probe point data objects and/or validate one or more probe point data objects for use with determination of traffic conditions. A determined probe point data object may be determined and/or updated with one or more probe point data objects which exclude any probe point data objects determined to correspond to an off-road location. A validated probe point data object may additionally or alternatively be determined using one or more first validation sequence check operations and using one or more second validation sequence check operations. The determined probe point data object and/or validated probe point data object may be used to determine one or more traffic conditions, and as a result, traffic condition determinations may be determined more accurately and efficiently.
A method and system are disclosed for collecting and maintaining road event information, where point-based road event location data from transportation authorities, construction companies, vehicle sensors, or combinations thereof, is susceptible to location sensor errors, digital map errors, and/or map mismatching errors. Errors less than a selected threshold are filtered by categorizing or grouping reports of point-based locations into segments along links in a representation of a road, providing improved accuracy of reporting of road events.
An approach is provided for in-parking navigation based on mobile device sensor data, the resulted semantic events, and an estimated parking occupancy level. The approach, for example, involves receiving parking information from in-parking navigation system(s) of vehicle(s) and/or mobile device(s) associated with the vehicle(s) traveling in a parking facility with no or a positioning satellite coverage below a threshold. The parking information indicates parking spot location(s) occupied by the vehicle(s). The in-parking navigation system(s) determines the at least one parking spot location based on tracking multi-modal trajectories of the mobile device(s). The multi-modal trajectories comprise vehicle trajectory segment(s) traveling within the parking facility and pedestrian trajectory segment(s) traveling to/from pedestrian entry or exit point(s) of the parking facility. The approach also involves determining an occupancy level of the parking facility based on the parking information. The approach further involves providing the occupancy level of the parking facility as an output.
An approach is provided for determining road work zone travel time reliability based on vehicle sensor data. The approach involves, for instance, aggregating telematic data generated by vehicles traveling within a geographic area, to detect a road work zone and/or active time period(s) thereof. The approach also involves identifying a start location and an end location of the road work zone. The approach further involves identifying a starting time point when one of the vehicles approaching the start location and an end time point when the vehicle passes the end location. The approach further involves calculating a time difference between the start and end time points as a travel time through the road work zone. The approach further involves aggregating the vehicle travel times, to compute work zone travel time reliability index(es) for the road work zone. The approach further involves providing the travel time reliability index(es) as an output.
Systems and methods for determining utilization of an area for vehicle parking are provided. For example, a method for determining utilization of an area for vehicle parking includes determining an expected level of utilization of an area for vehicle parking. The method also includes based on the determined expected level of utilization, analyzing one or more characteristics associated the area for vehicle parking. The method also includes based on the analysis, providing information for modifying the utility of the area for vehicle parking.
An approach is provided for pole extraction from a single image. The approach involves, for instance, processing an image using a machine learning model to detect one or more semantic keypoints associated with a pole-like object and to determine two-dimensional coordinate data for the one or more semantic keypoints. The approach also involves performing a monocular depth estimation to determine depth information for the one or more semantic keypoints based on the image. The approach further involves determining three-dimensional coordinate data for the one or more semantic keypoints based on the monocular depth information, the two-dimensional coordinate data, and camera parameter data. The approach yet further involves providing the three-dimensional coordinate data as an output.
An approach is provided for determining a confidence level for map feature identification in a map application. The approach involves, for instance, presenting an image of a map feature in a user interface of a device. The image is initially presented with a content of the image obscured from view. The approach also involves progressively un-obscuring the image in the user interface until an input identifying the map feature is received from a user via the user interface. The approach further involves determining a percentage of the image that is visible at a time the input is received from the user. The approach further involves personalizing an application to the user based on the feature identification confidence level.
A method, apparatus and computer program product are provided to automatically detect changes in width of road segments in real-time or near real-time using probe data, such as probe data collected from vehicle and/or mobile devices traveling along a road segment. Probe data collected in real-time or near real-time is partitioned in order to identify width-defining portions of the probe data. The width-defining portions may be representative of the laterally-extreme lanes of the road segment, such as the left-most lane and the right-most lane. The width-defining portions are compared to corresponding width-defining portions of historical probe data to determine measures indicative of whether a road segment has expanded or narrowed. Indications of detected segment width changes may be provided to drivers and/or other systems or users. For example, map data for the road segment may be updated to reflect a detected width expansion or narrowing of the road segment.
A method and apparatus for calculating an estimated travel time and a method and apparatus for defining a model configured to estimate a travel time are provided. The method for estimating a travel time includes obtaining a movement pattern for a train, determining one or more predicted times when the train will be present at a train crossing where a road path and a train path intersect using the movement pattern, obtaining vehicle historical data including an average time for a vehicle to travel one or more road segments adjoining the train crossing, obtaining a start time, and calculating the travel time for the one or more road segments based on the vehicle historical data, the start time, and the predicted time when the train will be present at the train crossing.
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